Intraoperative portable handheld and microscope-integrated OCT enhance the pediatric vitreoretinal surgeon's diagnostic abilities during examination under anesthesia and surgery, particularly in children who are challenging to examine preoperatively due to young age or ocular trauma. Improved OCT-guided visualization of vitreoretinal anatomic relationships has the potential to improve surgical safety and efficiency. In retinopathy of prematurity and other pediatric retinal vascular conditions, intraoperative OCT can be critical for distinguishing between retinoschisis and retinal detachment and highlighting abnormalities of the vitreoretinal interface that may contribute to development of tractional retinal detachments. During retinal detachment repair, intraoperative OCT aids identification of subtle retinal breaks, residual subretinal fluid, retained perfluorocarbon, preretinal membranes, and residual hyaloid, among other findings. In macular surgery, intraoperative OCT has demonstrated value in confirming completion or lack thereof of epiretinal and internal limiting membrane peeling and differentiating between lamellar and full-thickness macular holes. OCT-guided subretinal bleb formation and genetic vector delivery are critical to ensuring accurate localization of subretinal gene delivery for inherited retinal degenerations. Research on development of OCT-compatible surgical instruments, real-time three-dimensional volumetric OCT imaging, and integration with intraoperative OCT angiography are anticipated to further increase the utility of intraoperative OCT in pediatric vitreoretinal surgical decision-making.Particulate matters (PMs) air pollution is identified as the major threat to public health and climate. High-performance air filter technology based on various electrospun nanofibers is considered as an effective strategy to eliminate the effects of PMs air pollution. However, to date, nearly all the existing micro-/nanofibers are hard to meet both requirements of high PMs removal efficiency and long service life. In this work, we reported the production of laminated polyacrylonitrile(PAN)-boehmite nanoparticles (BNPs) nanofiber structured membrane by the electrospinning process. The dimension of PAN-BNPs nanofiber can be tunable from (0.09±0.03) μm to (0.81±0.11) μm by controlling the PAN and BNPs concentrations in precursors. The optimized PAN-BNPs nanofiber air filter with a basis weight of 1g/m2 demonstrates the attractive attributes of high PM2.5 removal efficiency up to 99.962% and low pressure drop of 58 Pa. Most importantly, after introducing the BNPs as electret, the removal efficiency is very stable under the air flow rate of 6 L/min. This PAN-BNPs nanofiber with a long electrostatic duration time offers an approach for fabricating future high-performance air filters.Hard x-ray photoelectron spectroscopy (HAXPES) is establishing itself as an essential technique for the characterisation of materials. The number of specialised photoelectron spectroscopy techniques making use of hard x-rays is steadily increasing and ever more complex experimental designs enable truly transformative insights into the chemical, electronic, magnetic, and structural nature of materials. This paper begins with a short historic perspective of HAXPES and spans from developments in the early days of photoelectron spectroscopy to provide an understanding of the origin and initial development of the technique to state-of-the-art instrumentation and experimental capabilities. The main motivation for and focus of this paper is to provide a picture of the technique in 2020, including a detailed overview of available experimental systems worldwide and insights into a range of specific measurement modi and approaches. We also aim to provide a glimpse into the future of the technique including possible developments and opportunities.
  MRI-based head models are used to predict the electric field (E-field) in the brain in Transcranial Current Stimulation (tCS). The standard field of view (FOV) of clinical MRI often only covers the head down to the skull base, which has usually lead to models truncated at the nose. Although recent pipelines can artificially extend the head model to the neck, the need for implementing full head models preserving skull holes such as the foramen magnum remains controversial. The objective is to analyze the impact of head model extent on E-field accuracy, with emphasis on specific electrode montages.
 
  A full head model containing an open foramen magnum and a cut head model with closed skull were compared in terms of predicted E-field. Several electrode montages, including fronto-occipital montages used in validation studies, were simulated.  https://www.selleckchem.com/products/th1760.html  Local and global metrics were used to evaluate the error for both E-field magnitude and distribution, along with tangential and normal components over different cortical arent on E-field accuracy depends on electrode montage. Standard cut head models provide sufficiently accurate predictions for both E-field magnitude and distribution in targeted brain areas. Fronto-occipital montages exhibited larger errors, which might be considered in further validation studies.Magnetic Resonance Fingerprinting (MRF) is a promising technique for fast quantitative imaging of human tissue. In general, MRF is based on a sequence of highly undersampled MR images which are analyzed with a pre-computed dictionary. MRF provides valuable diagnostic parameters such as the $T_1$ and $T_2$ MR relaxation times. However, uncertainty characterization of dictionary-based MRF estimates for $T_1$ and $T_2$ has not been achieved so far, which makes it challenging to assess if observed differences in these estimates are significant and may indicate pathological changes of the underlying tissue. We propose a Bayesian approach for the uncertainty quantification of dictionary-based MRF which leads to probability distributions for $T_1$ and $T_2$ in every voxel. The distributions can be used to make probability statements about the relaxation times, and to assign uncertainties to their dictionary-based MRF estimates. All uncertainty calculations are based on the pre-computed dictionary and the observed sequence of undersampled MR images, and they can be calculated in short time.